Is it possible to perform a dynamic "where/filter" in a dataframe ?
I am running a "like" operation to remove items that match specific strings
eventsDF.where(
~eventsDF.myColumn.like('FirstString%') &
~eventsDF.myColumn.like('anotherString%')
).count()
However I need to filter based on strings that come from another dataframe/list.
The solution that I was going for (which doesn't really work) involves a function that receives an index
#my_func[0] = "FirstString"
#my_func[1] = "anotherString"
def my_func(n):
return str(item[n])
newDf.where(
~newDf.useragent.like(str(my_func(1))+'%')
).count()
but I'm struggling to make it work by passing a range (mainly because it's a list instead of an integer)
newDf.where(
~newDf.useragent.like(str(my_func([i for i in range(2)])+'%'))
).count()
I don't want to go down the path of using "exec" or "eval" to perform it
str_likes = [~df.column.like(s) for s in strings] then reduce it into one expression reduce(lambda x, y: x & y, str_likes)
It's a little bit ugly but does what you want. You can also do this in a for loop like so
bool_expr = ~df.column.like(strings[0])
for s in strings[1:]:
bool_expr &= ~df.column.like(s)
df.where(bool_expr).count()
Related
I am trying to choose a random index for quicksort, but for some reason, the array is not sorting. In fact, the algorithm returns a different array (ex. input [2,1,4] and [1,1,4] is outputted) sometimes. Any help would be much appreciated. This algorithm works if, instead of choosing a random index, I always choose the first element of the array as the pivot.
def quicksort(array):
if len(array) < 2:
return array
else:
random_pivot_index = randint(0, len(array) - 1)
pivot = array[random_pivot_index]
less = [i for i in array[1:] if i =< pivot]
greater = [i for i in array[1:] if i > pivot]
return quicksort(less) + [pivot] + quicksort(greater)
less = [i for i in array[1:] if i =< pivot]
You're including elements equal to the pivot value in less here.
But here, you also include the pivot value explicitly in the result:
return quicksort(less) + [pivot] + quicksort(greater)
Instead try it with just:
return quicksort(less) + quicksort(greater)
Incidentally, though this does divide-and-conquer in the same way as QuickSort does, it's not really an implementation of that algorithm: Actual QuickSort sorts the elements in place - your version will suffer from the run-time overhead associated with allocating and concatenating the utility arrays.
I need to get a random sequence of 100 values from 10^-10 to 10^10 and storing to an Array using Scala. I tried following but it didn't work
Array(scala.math.pow(10,-10).doubleValue to scala.math.pow(10,10).intValue by scala.math.pow(10,5).toLong)
Can anyone help me to figure out how to do this correctly?
So you need to fill() the array with Random elements.
import scala.util.Random
val rndm = new Random(1911L)
Array.fill(100)(rndm.between(math.pow(10,-10), math.pow(10,10)))
//res0: Array[Double] = Array(6.08868427907728E9
// , 3.29548545155816E9
// , 9.52802903383275E9
// , 7.981295238889314E9
// , 1.9462480080050848E9
// . . .
This works because the 2nd parameter to the fill() method is "by-name", i.e. re-evaluated for every element.
UPDATE
Things aren't quite as clean if you don't have the .between() method (Scala 2.13).
Array.fill(100)(rndm.nextDouble())
.map(_ * math.pow(10,10))
Note that this actually has a floor of 0.0 instead of the desired 0.0000000001. It's very unlikely you'd have an entry that's too small, especially when taking only 100 samples. Still, there are steps you could take to insure that can't happen.
I am new to Scala and I would like to understand some basic stuff.
First of all, I need to calculate the average of a certain column of a DataFrame and use the result as a double type variable.
After some Internet research I was able to calculate the average and at the same time pass it into a List type Any by using the following command:
val avgX_List = mainDataFrame.groupBy().agg(mean("_c1")).collect().map(_(0)).toList
where "_c1" is the second column of my dataframe. This line of code returns a List with type List[Any].
To pass the result into a variable I used the following command:
var avgX = avgX_List(0)
hoping that the var avgX would be type double automatically but that didn't happen obviously.
So now let the questions begin:
What does map(_(0)) do? I know the basic definition of the map() transformation but I can't find an explanation with this exact argument
I know that by using .toList method in the end of the command my result will be a List with type Any. Is there a way that I could change this into List which contains type Double elements? Or even convert this one
Do you think that it would be much more appropriate to pass the column of my Dataframe into a List[Double] and then calculate the average of its elements?
Is the solution I showed above at any point of view correct based on my problem? I know that "it is working" is different from "correct solution"?
Summing up, I need to calculate the average of a certain column of a Dataframe and have the result as a double type variable.
Note that: I am Greek and I find it hard sometimes to understand some English coding "slang".
map(_(0)) is a shortcut for map( (r: Row) => r(0) ), which is in turn a shortcut for map( (r: Row) => r.apply(0) ). The apply method returns Any, and so you are losing the right type. Try using map(_.getAs[Double](0)) or map(_.getDouble(0)) instead.
Collecting all entries of the column and then computing the average would be highly counterproductive, because you'd have to send huge amounts of data to the master node, and then do all the calculations on this single central node. That would be the exact opposite of what Spark is good for.
You also don't need collect(...).toList, because you can access the 0-th entry directly (it doesn't matter whether you get it from an Array or from a List). Since you are collapsing everything into a single Row anyway, you could get rid of the map step entirely by reordering the methods a little bit:
val avgX = mainDataFrame.groupBy().agg(mean("_c1")).collect()(0).getDouble(0)
It can be written even shorter using the first method:
val avgX = mainDataFrame.groupBy().agg(mean("_c1")).first().getDouble(0)
#Any dataType in Scala can't be directly converted to Double.
#Use toString & then toDouble on final captured result.
#Eg-
#scala> x
#res22: Any = 1.0
#scala> x.toString.toDouble
#res23: Double = 1.0
#Note- Instead of using map().toList() directly use (0)(0) to get the final value from your resultset.
#TestSample(Scala)-
val wa = Array("one","two","two")
val wrdd = sc.parallelize(wa,3).map(x=>(x,1))
val wdf = wrdd.toDF("col1","col2")
val x = wdf.groupBy().agg(mean("col2")).collect()(0)(0).toString.toDouble
#O/p-
#scala> val x = wdf.groupBy().agg(mean("col2")).collect()(0)(0).toString.toDouble
#x: Double = 1.0
I am processing a large number of records (CDRS) that are essentially (who, where, how much), to save space I use a lookup to map the strings into integer and aggregate the traffic on a map of maps (who maps to a map (where maps how much)
type CDR = (String, String, Int)
type Lookup = scala.collection.mutable.HashMap[String, (Int, Float)]
type Traffic = scala.collection.mutable.HashMap[Int,scala.collection.mutable.HashMap[Int,Int]]enter code here
I have found a strange behavior, when I build the lookup tables in advance the code runs as expected, however when I start processing and build the maps on the fly it slows down as it processes the records.
I use the same function to build the lookup tables for this comparison. I essentially check if the code for the lookup is there, if not i create a new entry (it is a mutable map), like this:
def index(id: String, map: Lookup, reverse: Reverse): Int = {
if (map.contains(id)) {
map(id)._1
} else {
val number = if (map.keys.size == 0) 0 else reverse.keys.max + 1
reverse += ( number -> id)
map += (id -> (number, 0.toFloat))
number
}
}
Am I missing something here?
EDIT----> I can no longer reproduce the slowdown. I will assume I was either too tired or dumber than usual. Running time now seems to be same as I expected to be.
What is mapCellRvs? Default scala Map's .size (and .keys.size, which is the same thing) simply counts all elements by scanning them linearly.
Try replacing mapCellRvs.keys.size == 0 with mapCellRvs.isEmpty ...
Also, reverse.keys.max is linear as well. You may want to just remember the max somewhere separately, rather than compute it every time.
Currently i have two gremlin queries which will fetch two different values and i am populating in a map.
Scenario : A->B , A->C , A->D
My queries below,
graph.V().has(ID,A).out().label().toList()
Fetch the list of outE labels of A .
Result : List(B,C,D)
graph.traversal().V().has("ID",A).outE("interference").as("x").otherV().has("ID",B).select("x").values("value").headOption()
Given A and B , get the egde property value (A->B)
Return : 10
Is it possible that i can combine both there queries to get a return as Map[(B,10)(C,11)(D,12)]
I am facing some performance issue when i have two queries. Its taking more time
There is probably a better way to do this but I managed to get something with the following traversal:
gremlin> graph.traversal().V().has("ID","A").outE("interference").as("x").otherV().has("ID").label().as("y").select("x").by("value").as("z").select("y", "z").select(values);
==>[B,1]
==>[C,2]
I would wait for more answers though as I suspect there is a better traversal out there.
Below is working in scala
val b = StepLabel[Edge]()
val y = StepLabel[Label]()
val z = StepLabel[Integer]()
graph.traversal().V().has("ID",A).outE("interference").as(b)
.otherV().label().as(y)
.select(b).values("name").as(z)
.select((y,z)).toMap[String,Integer]
This will return Map[String,Int]